RT Journal Article
SR Electronic
T1 Simultaneously inferring T cell fate and clonality from single cell transcriptomes
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 025676
DO 10.1101/025676
A1 Michael J.T. Stubbington
A1 Tapio Lönnberg
A1 Valentina Proserpio
A1 Simon Clare
A1 Anneliese O. Speak
A1 Gordon Dougan
A1 Sarah A. Teichmann
YR 2015
UL http://biorxiv.org/content/early/2015/08/28/025676.abstract
AB The heterodimeric T cell receptor (TCR) comprises two protein chains that pair to determine the antigen specificity of each T lymphocyte. The enormous sequence diversity within TCR repertoires allows specific TCR sequences to be used as lineage markers for T cells that derive from a common progenitor. We have developed a computational method, called TraCeR, to reconstruct full-length, paired TCR sequences from T lymphocyte single-cell RNA-seq by combining existing assembly and alignment programs with a “synthetic genome” library comprising all possible TCR sequences. We validate this method with PCR to quantify its accuracy and sensitivity, and compare to other TCR sequencing methods. Our inferred TCR sequences reveal clonal relationships between T cells, which we put into the context of each cell’s functional state from the complete transcriptional landscape quantified from the remaining RNA-seq data. This provides a powerful tool to link T cell specificity with functional response in a variety of normal and pathological conditions. We demonstrate this by determining the distribution of members of expanded T cell clonotypes in response to Salmonella infection in the mouse. We show that members of the same clonotype span early activated CD4+ T cells, as well as mature effector and memory cells.